Xingyu Liu

LGBind: A structure-guided deep learning framework for accurate prediction of protein-ligand binding sites.

Unraveling the precise mechanisms of protein-ligand recognition remains a cornerstone of structure-based drug discovery. While recent deep learning advances have accelerated computational screening, a critical bottleneck persists: binding pocket characterization is often rigidly disconnected from the querying molecule'...

DSSMST: A Deterministic State Space Model for Self-Supervised Spatial Domain Identification in Spatial Transcriptomics.

CABA-Bind: Confounder-aligned backdoor adjustment for debiased RNA-ligand binding prediction.

CABA-Bind improves the reliability and ligand-specific interpretability of RNA-ligand molecular recognition modeling, and provided computational evidence suggesting that model-highlighted RNA regions around G17 and A53/A54 may contribute to ligand-associated recognition.

A multi-scale graph frequency network for structural and functional region analysis in spatial transcriptomics

E evaluation across diverse biological systems shows that SGFN achieves improved or competitive performance relative to representative baseline methods in reference-based benchmarks, and identifies biologically coherent spatial or functional regions in unlabeled datasets supported by marker-gene, spatial-autocorrelatio...